Mitigating long queues and waiting times with service resetting

Author:

Bonomo Ofek Lauber123ORCID,Pal Arnab1245ORCID,Reuveni Shlomi123ORCID

Affiliation:

1. School of Chemistry, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University , Tel Aviv 6997801, Israel

2. Center for the Physics and Chemistry of Living Systems, Tel Aviv University , Tel Aviv 6997801, Israel

3. The Sackler Center for Computational Molecular and Materials Science, Tel Aviv University , Tel Aviv 6997801, Israel

4. The Institute of Mathematical Sciences , IV Cross Road, CIT Campus, Taramani, Chennai 600113, Tamil Nadu, India

5. Homi Bhabha National Institute, Training School Complex , Anushakti Nagar, Mumbai 400094, India

Abstract

Abstract What determines the average length of a queue, which stretches in front of a service station? The answer to this question clearly depends on the average rate at which jobs arrive at the queue and on the average rate of service. Somewhat less obvious is the fact that stochastic fluctuations in service and arrival times are also important, and that these are a major source of backlogs and delays. Strategies that could mitigate fluctuations-induced delays are, thus in high demand as queue structures appear in various natural and man-made systems. Here, we demonstrate that a simple service resetting mechanism can reverse the deleterious effects of large fluctuations in service times, thus turning a marked drawback into a favorable advantage. This happens when stochastic fluctuations are intrinsic to the server, and we show that service resetting can then dramatically cut down average queue lengths and waiting times. Remarkably, this strategy is also useful in extreme situations where the variance, and possibly even mean, of the service time diverge—as resetting can then prevent queues from “blowing up.” We illustrate these results on the M/G/1 queue in which service times are general and arrivals are assumed to be Markovian. However, the main results and conclusions coming from our analysis are not specific to this particular model system. Thus, the results presented herein can be carried over to other queueing systems: in telecommunications, via computing, and all the way to molecular queues that emerge in enzymatic and metabolic cycles of living organisms.

Funder

Israel Science Foundation

European Research Council

Horizon 2020 Framework Programme

Publisher

Oxford University Press (OUP)

Reference66 articles.

Cited by 14 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimizing leapover lengths of Lévy flights with resetting;Physical Review E;2024-08-19

2. Queues with resetting: a perspective;Journal of Physics: Complexity;2024-05-07

3. Energy-based stochastic resetting can avoid noise-enhanced stability;Physical Review E;2024-02-07

4. Preface: stochastic resetting—theory and applications;Journal of Physics A: Mathematical and Theoretical;2024-01-25

5. Combining stochastic resetting with Metadynamics to speed-up molecular dynamics simulations;Nature Communications;2024-01-04

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